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Sci Rep. 2018 Feb 2;8(1):2232. doi: 10.1038/s41598-018-20625-5.

A computationally driven analysis of the polyphenol-protein interactome.

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The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto (TN), Italy.
Institute of Nutrition and Functional Foods (INAF), Québec, Canada.
Nestle Institute of Health Sciences, Lausanne, Switzerland.
Centre de Recherche du Centre Hospitalier Universitaire de Québec (CRCHUQ), Québec, Canada.
Department of Computer Science, University of Pisa, Pisa (PI), Italy.
Nestle Institute of Health Sciences, Lausanne, Switzerland.


Polyphenol-rich foods are part of many nutritional interventions aimed at improving health and preventing cardiometabolic diseases (CMDs). Polyphenols have oxidative, inflammatory, and/or metabolic effects. Research into the chemistry and biology of polyphenol bioactives is prolific but knowledge of their molecular interactions with proteins is limited. We mined public data to (i) identify proteins that interact with or metabolize polyphenols, (ii) mapped these proteins to pathways and networks, and (iii) annotated functions enriched within the resulting polyphenol-protein interactome. A total of 1,395 polyphenols and their metabolites were retrieved (using Phenol-Explorer and Dictionary of Natural Products) of which 369 polyphenols interacted with 5,699 unique proteins in 11,987 interactions as annotated in STITCH, Pathway Commons, and BindingDB. Pathway enrichment analysis using the KEGG repository identified a broad coverage of significant pathways of low specificity to particular polyphenol (sub)classes. When compared to drugs or micronutrients, polyphenols have pleiotropic effects across many biological processes related to metabolism and CMDs. These systems-wide effects were also found in the protein interactome of the polyphenol-rich citrus fruits, used as a case study. In sum, these findings provide a knowledgebase for identifying polyphenol classes (and polyphenol-rich foods) that individually or in combination influence metabolism.

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